By Leting Wu, Xiaowei Ying, Xintao Wu, Aidong Lu, Zhi-Hua Zhou (auth.), Joshua Zhexue Huang, Longbing Cao, Jaideep Srivastava (eds.)
The two-volume set LNAI 6634 and 6635 constitutes the refereed lawsuits of the fifteenth Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2011, held in Shenzhen, China in could 2011.
The overall of 32 revised complete papers and fifty eight revised brief papers have been rigorously reviewed and chosen from 331 submissions. The papers current new principles, unique learn effects, and useful improvement reviews from all KDD-related components together with info mining, computing device studying, man made intelligence and trend acceptance, info warehousing and databases, records, knoweldge engineering, habit sciences, visualization, and rising parts equivalent to social community analysis.
Read or Download Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings, Part II PDF
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Additional info for Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings, Part II
IIS-0705359, IIS0808661, IIS-0910453, and CCF-1019104, by the Defense Threat Reduction Agency under contract No. HDTRA1-10-1-0120, and by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053. This work is also partially supported by an IBM Faculty Award, and the Gordon and Betty Moore Foundation, in the eScience project. S. Government or other funding parties. S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Fortunately, when one of the matrices is very small, we can utilize the skewness to make an efficient M AP R E DUCE algorithm. This is exactly the case in HE IGEN ; the first matrix is very large, and the second is very small. The main idea is to distribute the second matrix by the distributed cache functionality in H ADOOP, and multiply each element of the first matrix with the corresponding rows of the second matrix. We call the resulting algorithm Cache-Based Matrix-Matrix multiplication, or CBMM.
This is the cause of the spurious eigenvalues in Lanczos-NO. Orthogonality can be recovered once the new basis vector is fully re-orthogonalized to all previous vectors. However, doing this becomes expensive as it requires O(m2 ) re-orthogonalizations, where m is the number of iterations. A better approach uses a quick test (line 10 of Algorithm 1) to selectively choose vectors that need to be re-orthogonalized to the new basis . This selective-reorthogonalization idea is shown in Algorithm 1.